| import json |
| import os |
| from typing import List |
|
|
| import datasets |
|
|
|
|
| _HF_ENDPOINT = os.getenv("HF_ENDPOINT", "https://huggingface.co") |
|
|
| _DESCRIPTION = "UltraChat: Large-scale, Informative, and Diverse Multi-round Dialogue Data." |
|
|
| _CITATION = """\ |
| @misc{UltraChat, |
| author = {Ding, Ning and Chen, Yulin and Xu, Bokai and Hu, Shengding and Qin, Yujia and Liu, Zhiyuan and Sun, Maosong and Zhou, Bowen}, |
| title = {UltraChat: A Large-scale Auto-generated Multi-round Dialogue Data}, |
| year = {2023}, |
| publisher = {GitHub}, |
| journal = {GitHub repository}, |
| howpublished = {\\url{https://github.com/thunlp/ultrachat}}, |
| } |
| """ |
|
|
| _HOMEPAGE = "{}/datasets/stingning/ultrachat".format(_HF_ENDPOINT) |
| _LICENSE = "cc-by-nc-4.0" |
| _BASE_DATA_URL = "{}/datasets/stingning/ultrachat/resolve/main/train_{{idx}}.jsonl".format(_HF_ENDPOINT) |
|
|
|
|
| class UltraChat(datasets.GeneratorBasedBuilder): |
| VERSION = datasets.Version("0.0.0") |
|
|
| def _info(self): |
| features = datasets.Features( |
| {"conversations": [{"from": datasets.Value("string"), "value": datasets.Value("string")}]} |
| ) |
| return datasets.DatasetInfo( |
| description=_DESCRIPTION, features=features, homepage=_HOMEPAGE, license=_LICENSE, citation=_CITATION |
| ) |
|
|
| def _split_generators(self, dl_manager: datasets.DownloadManager): |
| file_paths = [dl_manager.download(_BASE_DATA_URL.format(idx=idx)) for idx in range(10)] |
| return [datasets.SplitGenerator(name=datasets.Split.TRAIN, gen_kwargs={"filepaths": file_paths})] |
|
|
| def _generate_examples(self, filepaths: List[str]): |
| for filepath in filepaths: |
| with open(filepath, "r", encoding="utf-8") as f: |
| for row in f: |
| try: |
| data = json.loads(row) |
| except Exception: |
| continue |
| key: int = data["id"] |
| content: List[str] = data["data"] |
| if len(content) % 2 == 1: |
| content.pop(-1) |
| if len(content) < 2: |
| continue |
| conversations = [ |
| {"from": "human" if i % 2 == 0 else "gpt", "value": content[i]} for i in range(len(content)) |
| ] |
| yield key, {"conversations": conversations} |
|
|
